Cardiac arrest & resuscitation
External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study
An erratum to this article was published (Crit Care Med, 1 Jun 2024 ↗)
Authors' conclusion
The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation.
Study context
- Population
- Adults admitted to general wards, Yonsei University Health System Severance Hospital, South Korea, September 1, 2019 to August 31, 2020
- Exposure
- DeepCARS vs Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) for predicting in-hospital cardiac arrest (IHCA)
- Outcome
- IHCA-prediction performance (AUROC)
- Follow-up
- Within 24 hours of the alarm
Methodological detailssingle-centre, external validation
- Single-centre
- External validation
Main finding
As reported in the abstract
IHCA prediction (AUROC), RRS operating and nonoperating hours
- DeepCARS superior AUROC vs MEWS and NEWS, both during Rapid Response System (RRS) operating and nonoperating hours
- MEWS and NEWS performance varied across the two periods; DeepCARS consistent
Observational data: associations, not effects.
Original publication
External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study. Crit Care Med. Published 20 December 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →